User preference mining techniques for personalized applications View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2004-12

AUTHORS

Stefan Holland, Werner Kießling

ABSTRACT

Advanced personalized e-applications require comprehensive knowledge about their users’ likes and dislikes in order to provide individual product recommendations, personal customer advice, and custom-tailored product offers. In our approach we model such preferences as strict partial orders with “A is better than B” semantics, which has been proven to be very suitable in various e-applications. In this paper we present preference mining techniques for detecting strict partial order preferences in user log data. Real-life e-applications like online shops or financial services usually have large log data sets containing the transactions of their customers. Since the preference miner uses sophisticated SQL operations to execute all data intensive operations on database layer, our algorithms scale well even for such large log data sets. With preference mining personalized e-applications can gain valuable knowledge about their customers’ preferences, which can be applied for personalized product recommendations, individual customer service, or one-to-one marketing. More... »

PAGES

439-445

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf03250961

DOI

http://dx.doi.org/10.1007/bf03250961

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1030966041


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